{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 先检验一下Key的使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取地理编码\n",
    "import pandas as pd\n",
    "import requests\n",
    "key_hu =\"9dde73f86032748f9e8ac4a5ec77154e\"\n",
    "def geocode(address,city=None,batch=None,sig=None)->dict:\n",
    "    \"\"\"获取地理编码\"\"\"\n",
    "    url = 'https://restapi.amap.com/v3/geocode/geo?parameters'\n",
    "    params={\n",
    "        'key': key_hu,\n",
    "        'address':address,\n",
    "        'city':city,\n",
    "        'batch':batch,\n",
    "        'sig':sig,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': '1',\n",
       " 'info': 'OK',\n",
       " 'infocode': '10000',\n",
       " 'count': '1',\n",
       " 'geocodes': [{'formatted_address': '广东省广州市从化区中山大学南方学院',\n",
       "   'country': '中国',\n",
       "   'province': '广东省',\n",
       "   'citycode': '020',\n",
       "   'city': '广州市',\n",
       "   'district': '从化区',\n",
       "   'township': [],\n",
       "   'neighborhood': {'name': [], 'type': []},\n",
       "   'building': {'name': [], 'type': []},\n",
       "   'adcode': '440117',\n",
       "   'street': [],\n",
       "   'number': [],\n",
       "   'location': '113.679287,23.632575',\n",
       "   'level': '兴趣点'}]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "中大南方 = geocode('广东省广州市从化区中山大学南方学院')\n",
    "中大南方"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formatted_address</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>citycode</th>\n",
       "      <th>city</th>\n",
       "      <th>district</th>\n",
       "      <th>township</th>\n",
       "      <th>adcode</th>\n",
       "      <th>street</th>\n",
       "      <th>number</th>\n",
       "      <th>location</th>\n",
       "      <th>level</th>\n",
       "      <th>neighborhood.name</th>\n",
       "      <th>neighborhood.type</th>\n",
       "      <th>building.name</th>\n",
       "      <th>building.type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>广东省广州市从化区中山大学南方学院</td>\n",
       "      <td>中国</td>\n",
       "      <td>广东省</td>\n",
       "      <td>020</td>\n",
       "      <td>广州市</td>\n",
       "      <td>从化区</td>\n",
       "      <td>[]</td>\n",
       "      <td>440117</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>113.679287,23.632575</td>\n",
       "      <td>兴趣点</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   formatted_address country province citycode city district township  adcode  \\\n",
       "0  广东省广州市从化区中山大学南方学院      中国      广东省      020  广州市      从化区       []  440117   \n",
       "\n",
       "  street number              location level neighborhood.name  \\\n",
       "0     []     []  113.679287,23.632575   兴趣点                []   \n",
       "\n",
       "  neighborhood.type building.name building.type  \n",
       "0                []            []            []  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 表格化地理编码\n",
    "df = pd.json_normalize(中大南方['geocodes'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据以上测试，key可以使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 百度AI平台：https://ai.baidu.com/?track=cp:aipinzhuan|pf:pc|pp:AIpingtai|pu:title|ci:|kw:10005792\n",
    "#### 高德开放平台：https://lbs.amap.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 路径规划\n",
    "#### 外卖配送  从广东工业设计城配送到德云市场"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from pandas.io.json import json_normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备base url、params、response.json（） \n",
    "def cycling(origin,destination,sig=None)->dict:\n",
    "    url = 'https://restapi.amap.com/v3/direction/walking?parameters'\n",
    "    params={\n",
    "        'key':key_hu,\n",
    "        'origin':origin,\n",
    "        'destination':destination,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(起点)广东工业设计城_location: 113.349333,23.177833 (终点)德云市场_location: 113.258919,22.940310\n"
     ]
    }
   ],
   "source": [
    "# 获取两地地理坐标\n",
    "广东工业设计城 = geocode('广东工业设计城')\n",
    "德云市场 = geocode('德云市场')\n",
    "广东工业设计城_location = 广东工业设计城['geocodes'][0]['location']\n",
    "德云市场_location = 德云市场['geocodes'][0]['location']\n",
    "print(\"(起点)广东工业设计城_location:\",广东工业设计城_location,\"(终点)德云市场_location:\",德云市场_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instruction</th>\n",
       "      <th>orientation</th>\n",
       "      <th>road</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>polyline</th>\n",
       "      <th>action</th>\n",
       "      <th>assistant_action</th>\n",
       "      <th>walk_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>沿天源路向西南步行121米</td>\n",
       "      <td>西南</td>\n",
       "      <td>天源路</td>\n",
       "      <td>121</td>\n",
       "      <td>97</td>\n",
       "      <td>113.349136,23.178012;113.348911,23.177808;113....</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>沿天源路辅路向西南步行600米向右前方行走</td>\n",
       "      <td>西南</td>\n",
       "      <td>天源路辅路</td>\n",
       "      <td>600</td>\n",
       "      <td>480</td>\n",
       "      <td>113.348316,23.177218;113.348268,23.177057;113....</td>\n",
       "      <td>向右前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>沿天源路辅路向西南步行12米向左前方行走</td>\n",
       "      <td>西南</td>\n",
       "      <td>天源路辅路</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>113.346081,23.172235;113.346072,23.172201;113....</td>\n",
       "      <td>向左前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>沿天源路向西南步行159米向左前方行走</td>\n",
       "      <td>西南</td>\n",
       "      <td>天源路</td>\n",
       "      <td>159</td>\n",
       "      <td>127</td>\n",
       "      <td>113.34599,23.172183;113.345942,23.172023;113.3...</td>\n",
       "      <td>向左前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>沿长兴路向南步行107米右转</td>\n",
       "      <td>南</td>\n",
       "      <td>长兴路</td>\n",
       "      <td>107</td>\n",
       "      <td>86</td>\n",
       "      <td>113.345174,23.170951;113.345165,23.170747;113....</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>沿广珠公路向东北步行224米右转</td>\n",
       "      <td>东北</td>\n",
       "      <td>广珠公路</td>\n",
       "      <td>224</td>\n",
       "      <td>179</td>\n",
       "      <td>113.254757,22.944878;113.254874,22.945043;113....</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>沿彰义路向东南步行611米右转</td>\n",
       "      <td>东南</td>\n",
       "      <td>彰义路</td>\n",
       "      <td>611</td>\n",
       "      <td>489</td>\n",
       "      <td>113.256549,22.946033;113.256636,22.94592;113.2...</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>沿泰宁西路向西南步行31米左转</td>\n",
       "      <td>西南</td>\n",
       "      <td>泰宁西路</td>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>113.259527,22.941198;113.259258,22.941081</td>\n",
       "      <td>左转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>沿坤洲中路向南步行40米右转</td>\n",
       "      <td>南</td>\n",
       "      <td>坤洲中路</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>113.259253,22.941076;113.259284,22.940998;113....</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>向西步行51米到达目的地</td>\n",
       "      <td>西</td>\n",
       "      <td>[]</td>\n",
       "      <td>51</td>\n",
       "      <td>41</td>\n",
       "      <td>113.259397,22.940725;113.25931,22.940694;113.2...</td>\n",
       "      <td>[]</td>\n",
       "      <td>到达目的地</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               instruction orientation   road distance duration  \\\n",
       "0            沿天源路向西南步行121米          西南    天源路      121       97   \n",
       "1    沿天源路辅路向西南步行600米向右前方行走          西南  天源路辅路      600      480   \n",
       "2     沿天源路辅路向西南步行12米向左前方行走          西南  天源路辅路       12       10   \n",
       "3      沿天源路向西南步行159米向左前方行走          西南    天源路      159      127   \n",
       "4           沿长兴路向南步行107米右转           南    长兴路      107       86   \n",
       "..                     ...         ...    ...      ...      ...   \n",
       "121       沿广珠公路向东北步行224米右转          东北   广珠公路      224      179   \n",
       "122        沿彰义路向东南步行611米右转          东南    彰义路      611      489   \n",
       "123        沿泰宁西路向西南步行31米左转          西南   泰宁西路       31       25   \n",
       "124         沿坤洲中路向南步行40米右转           南   坤洲中路       40       32   \n",
       "125           向西步行51米到达目的地           西     []       51       41   \n",
       "\n",
       "                                              polyline  action  \\\n",
       "0    113.349136,23.178012;113.348911,23.177808;113....      []   \n",
       "1    113.348316,23.177218;113.348268,23.177057;113....  向右前方行走   \n",
       "2    113.346081,23.172235;113.346072,23.172201;113....  向左前方行走   \n",
       "3    113.34599,23.172183;113.345942,23.172023;113.3...  向左前方行走   \n",
       "4    113.345174,23.170951;113.345165,23.170747;113....      右转   \n",
       "..                                                 ...     ...   \n",
       "121  113.254757,22.944878;113.254874,22.945043;113....      右转   \n",
       "122  113.256549,22.946033;113.256636,22.94592;113.2...      右转   \n",
       "123          113.259527,22.941198;113.259258,22.941081      左转   \n",
       "124  113.259253,22.941076;113.259284,22.940998;113....      右转   \n",
       "125  113.259397,22.940725;113.25931,22.940694;113.2...      []   \n",
       "\n",
       "    assistant_action walk_type  \n",
       "0                 []         0  \n",
       "1                 []         0  \n",
       "2                 []         0  \n",
       "3                 []         0  \n",
       "4                 []         0  \n",
       "..               ...       ...  \n",
       "121               []         0  \n",
       "122               []         0  \n",
       "123               []         0  \n",
       "124               []         0  \n",
       "125            到达目的地         0  \n",
       "\n",
       "[126 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0              沿天源路向西南步行121米\n",
       "1      沿天源路辅路向西南步行600米向右前方行走\n",
       "2       沿天源路辅路向西南步行12米向左前方行走\n",
       "3        沿天源路向西南步行159米向左前方行走\n",
       "4             沿长兴路向南步行107米右转\n",
       "               ...          \n",
       "121         沿广珠公路向东北步行224米右转\n",
       "122          沿彰义路向东南步行611米右转\n",
       "123          沿泰宁西路向西南步行31米左转\n",
       "124           沿坤洲中路向南步行40米右转\n",
       "125             向西步行51米到达目的地\n",
       "Name: instruction, Length: 126, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 路径规划\n",
    "\n",
    "广东工业设计城_德云市场 = cycling(广东工业设计城_location ,德云市场_location)\n",
    "df_路径规划 = json_normalize(广东工业设计城_德云市场[\"route\"][\"paths\"][0]['steps'])\n",
    "display(df_路径规划)\n",
    "df_路径规划[\"instruction\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 静态地图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多边形搜索  静态地图\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "def staticmap(location,zoom,size=None,scale=1,markers=None,labels=None,paths=None,traffic=0,page=None,sig=None)->dict:\n",
    "    url = 'https://restapi.amap.com/v3/staticmap?parameters'\n",
    "    params={\n",
    "        'key':key_hu,\n",
    "        'location':location,\n",
    "        'zoom':zoom,\n",
    "        'size':size,\n",
    "        'scale':scale,\n",
    "        'markers':markers,\n",
    "        'labels':labels,\n",
    "        'paths':paths,\n",
    "        'traffic':traffic,\n",
    "        'sig':sig,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = Image.open(BytesIO(response.content))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=P size=400x400 at 0x22E40F6D988>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staticmap(location=德云市场_location,zoom=16)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
